Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations696
Missing cells808
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory582.6 KiB
Average record size in memory857.2 B

Variable types

Categorical5
DateTime2
Numeric8
Text5

Alerts

Acusado is highly overall correlated with ParticipantesHigh correlation
Altura is highly overall correlated with Longitud and 1 other fieldsHigh correlation
Comuna is highly overall correlated with LongitudHigh correlation
Longitud is highly overall correlated with Altura and 1 other fieldsHigh correlation
Participantes is highly overall correlated with Acusado and 1 other fieldsHigh correlation
Tipo de calle is highly overall correlated with AlturaHigh correlation
Victima is highly overall correlated with ParticipantesHigh correlation
Cantidad de victimas is highly imbalanced (87.6%) Imbalance
Altura has 567 (81.5%) missing values Missing
Cruce has 171 (24.6%) missing values Missing
Dirección normalizada has 8 (1.1%) missing values Missing
Longitud has 12 (1.7%) missing values Missing
Latitud has 12 (1.7%) missing values Missing
Victima has 9 (1.3%) missing values Missing
Acusado has 23 (3.3%) missing values Missing
HH has 23 (3.3%) zeros Zeros

Reproduction

Analysis started2024-10-17 16:00:44.873500
Analysis finished2024-10-17 16:03:22.634720
Duration2 minutes and 37.76 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Cantidad de victimas
Categorical

Imbalance 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
1
676 
2
 
19
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters696
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%

Length

2024-10-17T13:03:22.803428image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T13:03:22.992198image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%

Fecha
Date

Distinct598
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
Minimum2016-01-01 00:00:00
Maximum2021-12-30 00:00:00
2024-10-17T13:03:23.208732image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:03:23.504251image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Año
Real number (ℝ)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.1882
Minimum2016
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-10-17T13:03:23.752521image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2018
Q32020
95-th percentile2021
Maximum2021
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6837537
Coefficient of variation (CV)0.00083428972
Kurtosis-1.1149559
Mean2018.1882
Median Absolute Deviation (MAD)1
Skewness0.28590787
Sum1404659
Variance2.8350265
MonotonicityIncreasing
2024-10-17T13:03:23.963277image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2016 144
20.7%
2018 143
20.5%
2017 131
18.8%
2019 103
14.8%
2021 97
13.9%
2020 78
11.2%
ValueCountFrequency (%)
2016 144
20.7%
2017 131
18.8%
2018 143
20.5%
2019 103
14.8%
2020 78
11.2%
2021 97
13.9%
ValueCountFrequency (%)
2021 97
13.9%
2020 78
11.2%
2019 103
14.8%
2018 143
20.5%
2017 131
18.8%
2016 144
20.7%

Mes
Real number (ℝ)

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6925287
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-10-17T13:03:24.167331image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5713088
Coefficient of variation (CV)0.53362621
Kurtosis-1.2513063
Mean6.6925287
Median Absolute Deviation (MAD)3
Skewness-0.047244355
Sum4658
Variance12.754246
MonotonicityNot monotonic
2024-10-17T13:03:24.361878image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 78
11.2%
11 67
9.6%
8 65
9.3%
1 62
8.9%
5 60
8.6%
6 58
8.3%
2 56
8.0%
3 51
7.3%
10 51
7.3%
7 51
7.3%
Other values (2) 97
13.9%
ValueCountFrequency (%)
1 62
8.9%
2 56
8.0%
3 51
7.3%
4 50
7.2%
5 60
8.6%
6 58
8.3%
7 51
7.3%
8 65
9.3%
9 47
6.8%
10 51
7.3%
ValueCountFrequency (%)
12 78
11.2%
11 67
9.6%
10 51
7.3%
9 47
6.8%
8 65
9.3%
7 51
7.3%
6 58
8.3%
5 60
8.6%
4 50
7.2%
3 51
7.3%

Día
Real number (ℝ)

Distinct31
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.936782
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-10-17T13:03:24.571514image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.6396458
Coefficient of variation (CV)0.54211986
Kurtosis-1.1496069
Mean15.936782
Median Absolute Deviation (MAD)7
Skewness-0.032362106
Sum11092
Variance74.64348
MonotonicityNot monotonic
2024-10-17T13:03:24.803994image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 31
 
4.5%
17 30
 
4.3%
3 27
 
3.9%
11 27
 
3.9%
27 27
 
3.9%
12 26
 
3.7%
14 26
 
3.7%
9 25
 
3.6%
15 25
 
3.6%
10 25
 
3.6%
Other values (21) 427
61.4%
ValueCountFrequency (%)
1 18
2.6%
2 22
3.2%
3 27
3.9%
4 23
3.3%
5 18
2.6%
6 19
2.7%
7 23
3.3%
8 14
2.0%
9 25
3.6%
10 25
3.6%
ValueCountFrequency (%)
31 13
1.9%
30 16
2.3%
29 22
3.2%
28 25
3.6%
27 27
3.9%
26 21
3.0%
25 24
3.4%
24 20
2.9%
23 24
3.4%
22 23
3.3%

Hora
Date

Distinct324
Distinct (%)46.6%
Missing1
Missing (%)0.1%
Memory size61.0 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-14 07:12:00
2024-10-17T13:03:25.119110image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:03:25.409524image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

HH
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)3.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean11.657554
Minimum0
Maximum23
Zeros23
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-10-17T13:03:25.771594image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median11
Q317.5
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.7000901
Coefficient of variation (CV)0.57474236
Kurtosis-1.1564073
Mean11.657554
Median Absolute Deviation (MAD)6
Skewness0.042991407
Sum8102
Variance44.891207
MonotonicityNot monotonic
2024-10-17T13:03:25.966978image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
7 41
 
5.9%
6 40
 
5.7%
9 36
 
5.2%
5 35
 
5.0%
14 33
 
4.7%
12 32
 
4.6%
18 31
 
4.5%
10 31
 
4.5%
8 31
 
4.5%
17 30
 
4.3%
Other values (14) 355
51.0%
ValueCountFrequency (%)
0 23
3.3%
1 24
3.4%
2 17
2.4%
3 26
3.7%
4 23
3.3%
5 35
5.0%
6 40
5.7%
7 41
5.9%
8 31
4.5%
9 36
5.2%
ValueCountFrequency (%)
23 28
4.0%
22 30
4.3%
21 29
4.2%
20 26
3.7%
19 30
4.3%
18 31
4.5%
17 30
4.3%
16 30
4.3%
15 25
3.6%
14 33
4.7%
Distinct682
Distinct (%)98.1%
Missing1
Missing (%)0.1%
Memory size110.1 KiB
2024-10-17T13:03:27.240753image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length85
Median length52
Mean length28.902158
Min length8

Characters and Unicode

Total characters20087
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique671 ?
Unique (%)96.5%

Sample

1st rowAV PIEDRA BUENA Y AV FERNANDEZ DE LA CRUZ
2nd rowAV GRAL PAZ Y AV DE LOS CORRALES
3rd rowAV ENTRE RIOS 2034
4th rowAV LARRAZABAL Y GRAL VILLEGAS CONRADO
5th rowAV SAN JUAN Y PRESIDENTE LUIS SAENZ PEÑA
ValueCountFrequency (%)
av 617
 
16.4%
y 526
 
14.0%
de 121
 
3.2%
gral 97
 
2.6%
paz 70
 
1.9%
juan 48
 
1.3%
la 42
 
1.1%
au 40
 
1.1%
del 34
 
0.9%
san 31
 
0.8%
Other values (802) 2139
56.8%
2024-10-17T13:03:28.582407image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3086
 
15.4%
A 2315
 
11.5%
E 1077
 
5.4%
R 1064
 
5.3%
O 945
 
4.7%
L 756
 
3.8%
I 739
 
3.7%
N 699
 
3.5%
. 680
 
3.4%
V 658
 
3.3%
Other values (67) 8068
40.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3086
 
15.4%
A 2315
 
11.5%
E 1077
 
5.4%
R 1064
 
5.3%
O 945
 
4.7%
L 756
 
3.8%
I 739
 
3.7%
N 699
 
3.5%
. 680
 
3.4%
V 658
 
3.3%
Other values (67) 8068
40.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3086
 
15.4%
A 2315
 
11.5%
E 1077
 
5.4%
R 1064
 
5.3%
O 945
 
4.7%
L 756
 
3.8%
I 739
 
3.7%
N 699
 
3.5%
. 680
 
3.4%
V 658
 
3.3%
Other values (67) 8068
40.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3086
 
15.4%
A 2315
 
11.5%
E 1077
 
5.4%
R 1064
 
5.3%
O 945
 
4.7%
L 756
 
3.8%
I 739
 
3.7%
N 699
 
3.5%
. 680
 
3.4%
V 658
 
3.3%
Other values (67) 8068
40.2%

Tipo de calle
Categorical

High correlation 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size93.6 KiB
AVENIDA
429 
CALLE
136 
AUTOPISTA
66 
GRAL PAZ
65 

Length

Max length9
Median length7
Mean length6.8922414
Min length5

Characters and Unicode

Total characters4797
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAVENIDA
2nd rowGRAL PAZ
3rd rowAVENIDA
4th rowAVENIDA
5th rowAVENIDA

Common Values

ValueCountFrequency (%)
AVENIDA 429
61.6%
CALLE 136
 
19.5%
AUTOPISTA 66
 
9.5%
GRAL PAZ 65
 
9.3%

Length

2024-10-17T13:03:28.834367image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T13:03:29.039448image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
avenida 429
56.4%
calle 136
 
17.9%
autopista 66
 
8.7%
gral 65
 
8.5%
paz 65
 
8.5%

Most occurring characters

ValueCountFrequency (%)
A 1256
26.2%
E 565
11.8%
I 495
 
10.3%
V 429
 
8.9%
N 429
 
8.9%
D 429
 
8.9%
L 337
 
7.0%
C 136
 
2.8%
T 132
 
2.8%
P 131
 
2.7%
Other values (7) 458
 
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1256
26.2%
E 565
11.8%
I 495
 
10.3%
V 429
 
8.9%
N 429
 
8.9%
D 429
 
8.9%
L 337
 
7.0%
C 136
 
2.8%
T 132
 
2.8%
P 131
 
2.7%
Other values (7) 458
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1256
26.2%
E 565
11.8%
I 495
 
10.3%
V 429
 
8.9%
N 429
 
8.9%
D 429
 
8.9%
L 337
 
7.0%
C 136
 
2.8%
T 132
 
2.8%
P 131
 
2.7%
Other values (7) 458
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1256
26.2%
E 565
11.8%
I 495
 
10.3%
V 429
 
8.9%
N 429
 
8.9%
D 429
 
8.9%
L 337
 
7.0%
C 136
 
2.8%
T 132
 
2.8%
P 131
 
2.7%
Other values (7) 458
 
9.5%

Calle
Text

Distinct279
Distinct (%)40.1%
Missing1
Missing (%)0.1%
Memory size99.8 KiB
2024-10-17T13:03:29.805319image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length42
Median length32
Mean length16.054676
Min length4

Characters and Unicode

Total characters11158
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)24.6%

Sample

1st rowPIEDRA BUENA AV.
2nd rowPAZ, GRAL. AV.
3rd rowENTRE RIOS AV.
4th rowLARRAZABAL AV.
5th rowSAN JUAN AV.
ValueCountFrequency (%)
av 419
 
22.3%
gral 86
 
4.6%
de 72
 
3.8%
paz 58
 
3.1%
autopista 53
 
2.8%
juan 38
 
2.0%
del 24
 
1.3%
moreno 23
 
1.2%
la 23
 
1.2%
san 22
 
1.2%
Other values (375) 1063
56.5%
2024-10-17T13:03:30.869094image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1622
14.5%
1186
 
10.6%
E 798
 
7.2%
R 795
 
7.1%
O 704
 
6.3%
I 590
 
5.3%
. 586
 
5.3%
V 527
 
4.7%
L 518
 
4.6%
N 503
 
4.5%
Other values (28) 3329
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1622
14.5%
1186
 
10.6%
E 798
 
7.2%
R 795
 
7.1%
O 704
 
6.3%
I 590
 
5.3%
. 586
 
5.3%
V 527
 
4.7%
L 518
 
4.6%
N 503
 
4.5%
Other values (28) 3329
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1622
14.5%
1186
 
10.6%
E 798
 
7.2%
R 795
 
7.1%
O 704
 
6.3%
I 590
 
5.3%
. 586
 
5.3%
V 527
 
4.7%
L 518
 
4.6%
N 503
 
4.5%
Other values (28) 3329
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1622
14.5%
1186
 
10.6%
E 798
 
7.2%
R 795
 
7.1%
O 704
 
6.3%
I 590
 
5.3%
. 586
 
5.3%
V 527
 
4.7%
L 518
 
4.6%
N 503
 
4.5%
Other values (28) 3329
29.8%

Altura
Real number (ℝ)

High correlation  Missing 

Distinct126
Distinct (%)97.7%
Missing567
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean3336.6357
Minimum30
Maximum16080
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-10-17T13:03:31.113396image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile365
Q11359
median2551
Q34500
95-th percentile9388.4
Maximum16080
Range16050
Interquartile range (IQR)3141

Descriptive statistics

Standard deviation3060.6418
Coefficient of variation (CV)0.91728379
Kurtosis5.6986682
Mean3336.6357
Median Absolute Deviation (MAD)1433
Skewness2.1594365
Sum430426
Variance9367528.2
MonotonicityNot monotonic
2024-10-17T13:03:31.391654image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 2
 
0.3%
901 2
 
0.3%
4300 2
 
0.3%
156 1
 
0.1%
2034 1
 
0.1%
1483 1
 
0.1%
3900 1
 
0.1%
2384 1
 
0.1%
1410 1
 
0.1%
4233 1
 
0.1%
Other values (116) 116
 
16.7%
(Missing) 567
81.5%
ValueCountFrequency (%)
30 1
0.1%
133 1
0.1%
150 1
0.1%
156 1
0.1%
300 1
0.1%
305 1
0.1%
365 2
0.3%
390 1
0.1%
466 1
0.1%
550 1
0.1%
ValueCountFrequency (%)
16080 1
0.1%
15200 1
0.1%
14800 1
0.1%
14723 1
0.1%
11200 1
0.1%
11050 1
0.1%
10900 1
0.1%
7121 1
0.1%
7013 1
0.1%
6950 1
0.1%

Cruce
Text

Missing 

Distinct317
Distinct (%)60.4%
Missing171
Missing (%)24.6%
Memory size93.2 KiB
2024-10-17T13:03:32.117628image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length42
Median length30
Mean length13.933333
Min length3

Characters and Unicode

Total characters7315
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)39.6%

Sample

1st rowFERNANDEZ DE LA CRUZ, F., GRAL. AV.
2nd rowDE LOS CORRALES AV.
3rd rowVILLEGAS, CONRADO, GRAL.
4th rowSAENZ PE?A, LUIS, PRES.
5th rowESCALADA AV.
ValueCountFrequency (%)
av 216
 
18.0%
de 41
 
3.4%
gral 28
 
2.3%
la 16
 
1.3%
dr 15
 
1.3%
paz 14
 
1.2%
juan 13
 
1.1%
del 11
 
0.9%
cnel 11
 
0.9%
pres 9
 
0.8%
Other values (431) 823
68.8%
2024-10-17T13:03:33.130220image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1079
14.8%
672
 
9.2%
E 581
 
7.9%
R 546
 
7.5%
O 471
 
6.4%
N 395
 
5.4%
L 374
 
5.1%
I 365
 
5.0%
. 319
 
4.4%
V 315
 
4.3%
Other values (30) 2198
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1079
14.8%
672
 
9.2%
E 581
 
7.9%
R 546
 
7.5%
O 471
 
6.4%
N 395
 
5.4%
L 374
 
5.1%
I 365
 
5.0%
. 319
 
4.4%
V 315
 
4.3%
Other values (30) 2198
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1079
14.8%
672
 
9.2%
E 581
 
7.9%
R 546
 
7.5%
O 471
 
6.4%
N 395
 
5.4%
L 374
 
5.1%
I 365
 
5.0%
. 319
 
4.4%
V 315
 
4.3%
Other values (30) 2198
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1079
14.8%
672
 
9.2%
E 581
 
7.9%
R 546
 
7.5%
O 471
 
6.4%
N 395
 
5.4%
L 374
 
5.1%
I 365
 
5.0%
. 319
 
4.4%
V 315
 
4.3%
Other values (30) 2198
30.0%
Distinct635
Distinct (%)92.3%
Missing8
Missing (%)1.1%
Memory size111.0 KiB
2024-10-17T13:03:34.140041image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length75
Median length51
Mean length30.401163
Min length8

Characters and Unicode

Total characters20916
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique594 ?
Unique (%)86.3%

Sample

1st rowPIEDRA BUENA AV. y FERNANDEZ DE LA CRUZ, F., GRAL. AV.
2nd rowPAZ, GRAL. AV. y DE LOS CORRALES AV.
3rd rowENTRE RIOS AV. 2034
4th rowLARRAZABAL AV. y VILLEGAS, CONRADO, GRAL.
5th rowSAN JUAN AV. y SAENZ PEÑA, LUIS, PRES.
ValueCountFrequency (%)
av 642
 
17.0%
y 537
 
14.2%
de 118
 
3.1%
gral 114
 
3.0%
paz 72
 
1.9%
autopista 54
 
1.4%
juan 51
 
1.3%
la 38
 
1.0%
del 35
 
0.9%
san 31
 
0.8%
Other values (736) 2094
55.3%
2024-10-17T13:03:35.501136image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3100
14.8%
A 2719
13.0%
E 1393
 
6.7%
R 1355
 
6.5%
O 1172
 
5.6%
I 958
 
4.6%
. 923
 
4.4%
N 908
 
4.3%
L 889
 
4.3%
V 854
 
4.1%
Other values (40) 6645
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20916
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3100
14.8%
A 2719
13.0%
E 1393
 
6.7%
R 1355
 
6.5%
O 1172
 
5.6%
I 958
 
4.6%
. 923
 
4.4%
N 908
 
4.3%
L 889
 
4.3%
V 854
 
4.1%
Other values (40) 6645
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20916
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3100
14.8%
A 2719
13.0%
E 1393
 
6.7%
R 1355
 
6.5%
O 1172
 
5.6%
I 958
 
4.6%
. 923
 
4.4%
N 908
 
4.3%
L 889
 
4.3%
V 854
 
4.1%
Other values (40) 6645
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20916
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3100
14.8%
A 2719
13.0%
E 1393
 
6.7%
R 1355
 
6.5%
O 1172
 
5.6%
I 958
 
4.6%
. 923
 
4.4%
N 908
 
4.3%
L 889
 
4.3%
V 854
 
4.1%
Other values (40) 6645
31.8%

Comuna
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)2.2%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean7.4466859
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2024-10-17T13:03:35.713528image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q311
95-th percentile15
Maximum15
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3751772
Coefficient of variation (CV)0.58753346
Kurtosis-1.1256326
Mean7.4466859
Median Absolute Deviation (MAD)4
Skewness0.099281129
Sum5168
Variance19.142175
MonotonicityNot monotonic
2024-10-17T13:03:35.898071image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 90
12.9%
4 76
10.9%
9 73
10.5%
8 65
9.3%
7 60
8.6%
3 45
 
6.5%
15 44
 
6.3%
13 40
 
5.7%
12 37
 
5.3%
14 35
 
5.0%
Other values (5) 129
18.5%
ValueCountFrequency (%)
1 90
12.9%
2 25
 
3.6%
3 45
6.5%
4 76
10.9%
5 22
 
3.2%
6 21
 
3.0%
7 60
8.6%
8 65
9.3%
9 73
10.5%
10 29
 
4.2%
ValueCountFrequency (%)
15 44
6.3%
14 35
5.0%
13 40
5.7%
12 37
5.3%
11 32
4.6%
10 29
 
4.2%
9 73
10.5%
8 65
9.3%
7 60
8.6%
6 21
 
3.0%

Longitud
Real number (ℝ)

High correlation  Missing 

Distinct604
Distinct (%)88.3%
Missing12
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean-58.441545
Minimum-58.529942
Maximum-58.356082
Zeros0
Zeros (%)0.0%
Negative684
Negative (%)98.3%
Memory size61.0 KiB
2024-10-17T13:03:36.129416image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-58.529942
5-th percentile-58.51927
Q1-58.476218
median-58.444513
Q3-58.401841
95-th percentile-58.371644
Maximum-58.356082
Range0.17386038
Interquartile range (IQR)0.074376782

Descriptive statistics

Standard deviation0.04614353
Coefficient of variation (CV)-0.00078956725
Kurtosis-1.0795143
Mean-58.441545
Median Absolute Deviation (MAD)0.0381611
Skewness-0.06269671
Sum-39974.017
Variance0.0021292254
MonotonicityNot monotonic
2024-10-17T13:03:36.491506image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-58.44451316 5
 
0.7%
-58.50877521 4
 
0.6%
-58.5007381 4
 
0.6%
-58.46743471 4
 
0.6%
-58.52340614 3
 
0.4%
-58.38526125 3
 
0.4%
-58.46963952 3
 
0.4%
-58.46749188 3
 
0.4%
-58.40623949 3
 
0.4%
-58.48727942 3
 
0.4%
Other values (594) 649
93.2%
(Missing) 12
 
1.7%
ValueCountFrequency (%)
-58.52994219 1
0.1%
-58.52933723 1
0.1%
-58.52932872 2
0.3%
-58.52931141 1
0.1%
-58.52927982 2
0.3%
-58.52922765 1
0.1%
-58.52909134 1
0.1%
-58.52887773 1
0.1%
-58.52866211 1
0.1%
-58.52844623 1
0.1%
ValueCountFrequency (%)
-58.35608181 1
0.1%
-58.35791192 1
0.1%
-58.35881506 1
0.1%
-58.35975012 1
0.1%
-58.3597716 1
0.1%
-58.3600465 1
0.1%
-58.36154069 1
0.1%
-58.36217754 1
0.1%
-58.36244648 1
0.1%
-58.36376488 1
0.1%

Latitud
Real number (ℝ)

Missing 

Distinct604
Distinct (%)88.3%
Missing12
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean-34.619632
Minimum-34.70525
Maximum-34.534654
Zeros0
Zeros (%)0.0%
Negative684
Negative (%)98.3%
Memory size61.0 KiB
2024-10-17T13:03:36.910584image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-34.70525
5-th percentile-34.679218
Q1-34.643689
median-34.622928
Q3-34.596799
95-th percentile-34.551647
Maximum-34.534654
Range0.17059593
Interquartile range (IQR)0.046890977

Descriptive statistics

Standard deviation0.035288848
Coefficient of variation (CV)-0.0010193305
Kurtosis-0.27152325
Mean-34.619632
Median Absolute Deviation (MAD)0.023784395
Skewness0.22691437
Sum-23679.828
Variance0.0012453028
MonotonicityNot monotonic
2024-10-17T13:03:37.211776image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-34.68475866 5
 
0.7%
-34.66977709 4
 
0.6%
-34.5497951 4
 
0.6%
-34.53476874 4
 
0.6%
-34.59798754 3
 
0.4%
-34.5780581 3
 
0.4%
-34.63070603 3
 
0.4%
-34.63551751 3
 
0.4%
-34.65076549 3
 
0.4%
-34.63652467 3
 
0.4%
Other values (594) 649
93.2%
(Missing) 12
 
1.7%
ValueCountFrequency (%)
-34.70524971 2
0.3%
-34.69843756 3
0.4%
-34.69640164 1
 
0.1%
-34.6937413 1
 
0.1%
-34.69156793 1
 
0.1%
-34.69153196 3
0.4%
-34.68795245 2
0.3%
-34.68757022 1
 
0.1%
-34.68550712 1
 
0.1%
-34.68482689 1
 
0.1%
ValueCountFrequency (%)
-34.53465378 1
 
0.1%
-34.53476874 4
0.6%
-34.53825652 2
0.3%
-34.54048625 2
0.3%
-34.5407313 1
 
0.1%
-34.54153077 1
 
0.1%
-34.54200276 1
 
0.1%
-34.54321216 1
 
0.1%
-34.54332766 1
 
0.1%
-34.54395182 1
 
0.1%

Participantes
Categorical

High correlation 

Distinct40
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size97.2 KiB
PEATON-PASAJEROS
105 
MOTO-AUTO
83 
MOTO-CARGAS
78 
PEATON-AUTO
77 
MOTO-PASAJEROS
46 
Other values (35)
307 

Length

Max length19
Median length18
Mean length12.244253
Min length5

Characters and Unicode

Total characters8522
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.9%

Sample

1st rowMOTO-AUTO
2nd rowAUTO-PASAJEROS
3rd rowMOTO-AUTO
4th rowMOTO-SD
5th rowMOTO-PASAJEROS

Common Values

ValueCountFrequency (%)
PEATON-PASAJEROS 105
15.1%
MOTO-AUTO 83
11.9%
MOTO-CARGAS 78
11.2%
PEATON-AUTO 77
11.1%
MOTO-PASAJEROS 46
 
6.6%
MOTO-OBJETO FIJO 40
 
5.7%
PEATON-CARGAS 38
 
5.5%
AUTO-AUTO 31
 
4.5%
PEATON-MOTO 30
 
4.3%
MOTO-MOTO 25
 
3.6%
Other values (30) 143
20.5%

Length

2024-10-17T13:03:37.460112image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
peaton-pasajeros 105
13.8%
moto-auto 83
10.9%
moto-cargas 78
10.3%
peaton-auto 77
10.1%
fijo 63
 
8.3%
moto-pasajeros 46
 
6.1%
moto-objeto 40
 
5.3%
peaton-cargas 38
 
5.0%
auto-auto 31
 
4.1%
peaton-moto 30
 
4.0%
Other values (31) 168
22.1%

Most occurring characters

ValueCountFrequency (%)
O 1615
19.0%
A 1241
14.6%
T 1011
11.9%
- 679
8.0%
E 554
 
6.5%
S 541
 
6.3%
P 455
 
5.3%
M 364
 
4.3%
R 338
 
4.0%
J 304
 
3.6%
Other values (12) 1420
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8522
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1615
19.0%
A 1241
14.6%
T 1011
11.9%
- 679
8.0%
E 554
 
6.5%
S 541
 
6.3%
P 455
 
5.3%
M 364
 
4.3%
R 338
 
4.0%
J 304
 
3.6%
Other values (12) 1420
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8522
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1615
19.0%
A 1241
14.6%
T 1011
11.9%
- 679
8.0%
E 554
 
6.5%
S 541
 
6.3%
P 455
 
5.3%
M 364
 
4.3%
R 338
 
4.0%
J 304
 
3.6%
Other values (12) 1420
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8522
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1615
19.0%
A 1241
14.6%
T 1011
11.9%
- 679
8.0%
E 554
 
6.5%
S 541
 
6.3%
P 455
 
5.3%
M 364
 
4.3%
R 338
 
4.0%
J 304
 
3.6%
Other values (12) 1420
16.7%

Victima
Categorical

High correlation  Missing 

Distinct8
Distinct (%)1.2%
Missing9
Missing (%)1.3%
Memory size92.3 KiB
MOTO
295 
PEATON
264 
AUTO
84 
BICICLETA
 
29
CARGAS
 
7
Other values (3)
 
8

Length

Max length9
Median length4
Mean length5.0451237
Min length4

Characters and Unicode

Total characters3466
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowMOTO
2nd rowAUTO
3rd rowMOTO
4th rowMOTO
5th rowMOTO

Common Values

ValueCountFrequency (%)
MOTO 295
42.4%
PEATON 264
37.9%
AUTO 84
 
12.1%
BICICLETA 29
 
4.2%
CARGAS 7
 
1.0%
PASAJEROS 5
 
0.7%
MOVIL 2
 
0.3%
MULTIPLE 1
 
0.1%
(Missing) 9
 
1.3%

Length

2024-10-17T13:03:37.672690image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T13:03:37.882647image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
moto 295
42.9%
peaton 264
38.4%
auto 84
 
12.2%
bicicleta 29
 
4.2%
cargas 7
 
1.0%
pasajeros 5
 
0.7%
movil 2
 
0.3%
multiple 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
O 945
27.3%
T 673
19.4%
A 401
11.6%
E 299
 
8.6%
M 298
 
8.6%
P 270
 
7.8%
N 264
 
7.6%
U 85
 
2.5%
C 65
 
1.9%
I 61
 
1.8%
Other values (7) 105
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3466
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 945
27.3%
T 673
19.4%
A 401
11.6%
E 299
 
8.6%
M 298
 
8.6%
P 270
 
7.8%
N 264
 
7.6%
U 85
 
2.5%
C 65
 
1.9%
I 61
 
1.8%
Other values (7) 105
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3466
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 945
27.3%
T 673
19.4%
A 401
11.6%
E 299
 
8.6%
M 298
 
8.6%
P 270
 
7.8%
N 264
 
7.6%
U 85
 
2.5%
C 65
 
1.9%
I 61
 
1.8%
Other values (7) 105
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3466
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 945
27.3%
T 673
19.4%
A 401
11.6%
E 299
 
8.6%
M 298
 
8.6%
P 270
 
7.8%
N 264
 
7.6%
U 85
 
2.5%
C 65
 
1.9%
I 61
 
1.8%
Other values (7) 105
 
3.0%

Acusado
Categorical

High correlation  Missing 

Distinct9
Distinct (%)1.3%
Missing23
Missing (%)3.3%
Memory size93.3 KiB
AUTO
203 
PASAJEROS
173 
CARGAS
146 
OBJETO FIJO
63 
MOTO
57 
Other values (4)
31 

Length

Max length11
Median length9
Mean length6.5274889
Min length4

Characters and Unicode

Total characters4393
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAUTO
2nd rowPASAJEROS
3rd rowAUTO
4th rowPASAJEROS
5th rowOBJETO FIJO

Common Values

ValueCountFrequency (%)
AUTO 203
29.2%
PASAJEROS 173
24.9%
CARGAS 146
21.0%
OBJETO FIJO 63
 
9.1%
MOTO 57
 
8.2%
MULTIPLE 17
 
2.4%
BICICLETA 7
 
1.0%
OTRO 6
 
0.9%
TREN 1
 
0.1%
(Missing) 23
 
3.3%

Length

2024-10-17T13:03:38.128032image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T13:03:38.361318image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
auto 203
27.6%
pasajeros 173
23.5%
cargas 146
19.8%
objeto 63
 
8.6%
fijo 63
 
8.6%
moto 57
 
7.7%
multiple 17
 
2.3%
bicicleta 7
 
1.0%
otro 6
 
0.8%
tren 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 848
19.3%
O 691
15.7%
S 492
11.2%
T 354
8.1%
R 326
 
7.4%
J 299
 
6.8%
E 261
 
5.9%
U 220
 
5.0%
P 190
 
4.3%
C 160
 
3.6%
Other values (8) 552
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4393
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 848
19.3%
O 691
15.7%
S 492
11.2%
T 354
8.1%
R 326
 
7.4%
J 299
 
6.8%
E 261
 
5.9%
U 220
 
5.0%
P 190
 
4.3%
C 160
 
3.6%
Other values (8) 552
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4393
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 848
19.3%
O 691
15.7%
S 492
11.2%
T 354
8.1%
R 326
 
7.4%
J 299
 
6.8%
E 261
 
5.9%
U 220
 
5.0%
P 190
 
4.3%
C 160
 
3.6%
Other values (8) 552
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4393
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 848
19.3%
O 691
15.7%
S 492
11.2%
T 354
8.1%
R 326
 
7.4%
J 299
 
6.8%
E 261
 
5.9%
U 220
 
5.0%
P 190
 
4.3%
C 160
 
3.6%
Other values (8) 552
12.6%
Distinct605
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size106.4 KiB
2024-10-17T13:03:39.260517image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length38
Median length26
Mean length25.724138
Min length8

Characters and Unicode

Total characters17904
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique546 ?
Unique (%)78.4%

Sample

1st row-34.68757022, -58.47533969
2nd row-34.66977709, -58.50877521
3rd row-34.63189362, -58.39040293
4th row-34.68092974, -58.46503904
5th row-34.62246630, -58.38718297
ValueCountFrequency (%)
nan 24
 
1.7%
34.68475866 5
 
0.4%
58.44451316 5
 
0.4%
58.46743471 4
 
0.3%
58.50877521 4
 
0.3%
58.50073810 4
 
0.3%
34.54979510 4
 
0.3%
34.53476874 4
 
0.3%
34.66977709 4
 
0.3%
58.41657793 3
 
0.2%
Other values (1199) 1331
95.6%
2024-10-17T13:03:40.322870image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 2083
11.6%
5 1938
10.8%
3 1718
9.6%
8 1584
8.8%
6 1441
8.0%
. 1368
7.6%
- 1368
7.6%
7 1028
 
5.7%
9 1011
 
5.6%
1 1002
 
5.6%
Other values (6) 3363
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 2083
11.6%
5 1938
10.8%
3 1718
9.6%
8 1584
8.8%
6 1441
8.0%
. 1368
7.6%
- 1368
7.6%
7 1028
 
5.7%
9 1011
 
5.6%
1 1002
 
5.6%
Other values (6) 3363
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 2083
11.6%
5 1938
10.8%
3 1718
9.6%
8 1584
8.8%
6 1441
8.0%
. 1368
7.6%
- 1368
7.6%
7 1028
 
5.7%
9 1011
 
5.6%
1 1002
 
5.6%
Other values (6) 3363
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 2083
11.6%
5 1938
10.8%
3 1718
9.6%
8 1584
8.8%
6 1441
8.0%
. 1368
7.6%
- 1368
7.6%
7 1028
 
5.7%
9 1011
 
5.6%
1 1002
 
5.6%
Other values (6) 3363
18.8%

Interactions

2024-10-17T13:02:43.250445image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:00:47.407603image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:01.404095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:16.886905image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:37.704005image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:50.172466image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:53.878596image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:05.659387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:46.824320image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:00:47.876460image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:01.585617image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:17.178202image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:37.930766image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:50.370960image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:54.093022image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:09.366402image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:50.846910image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:00:48.098536image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:01.792078image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:17.352567image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:38.146255image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:50.548524image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:54.419659image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:13.549264image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:54.586018image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:00:48.503674image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:02.109610image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:17.544943image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:38.342196image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:50.742966image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:54.614647image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:17.073504image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:58.780335image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:00:48.728843image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:02.409326image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:17.731634image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:38.540179image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:50.920528image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:54.791214image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:20.531152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:59.669551image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:00:48.921335image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:02.663646image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:18.321670image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:38.741638image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:51.119583image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:54.974709image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:21.403901image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:03:03.965443image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:00:49.130999image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:02.929933image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:18.577979image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:38.932426image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:51.315531image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:55.164326image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:24.888051image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:03:12.622139image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:00:55.023180image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:09.545519image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:29.531085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:44.318337image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:01:52.598405image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:00.540348image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-17T13:02:33.862006image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-10-17T13:03:40.510449image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
AcusadoAlturaAñoCantidad de victimasComunaDíaHHLatitudLongitudMesParticipantesTipo de calleVictima
Acusado1.0000.2130.0900.0000.0670.0000.1270.1840.1840.0190.9800.1200.166
Altura0.2131.0000.0570.1840.427-0.0730.074-0.137-0.5320.0770.2150.5280.000
Año0.0900.0571.0000.0550.005-0.043-0.0390.0520.011-0.0470.1160.0310.000
Cantidad de victimas0.0000.1840.0551.0000.0000.0000.0610.2550.2550.0750.2170.0000.217
Comuna0.0670.4270.0050.0001.0000.003-0.0400.271-0.7150.0570.0810.2620.083
Día0.000-0.073-0.0430.0000.0031.000-0.023-0.022-0.032-0.0180.0210.0000.000
HH0.1270.074-0.0390.061-0.040-0.0231.0000.0310.0620.0260.1410.0330.111
Latitud0.184-0.1370.0520.2550.271-0.0220.0311.0000.117-0.0010.0750.3050.000
Longitud0.184-0.5320.0110.255-0.715-0.0320.0620.1171.000-0.0620.0750.3050.000
Mes0.0190.077-0.0470.0750.057-0.0180.026-0.001-0.0621.0000.0470.0440.000
Participantes0.9800.2150.1160.2170.0810.0210.1410.0750.0750.0471.0000.1950.973
Tipo de calle0.1200.5280.0310.0000.2620.0000.0330.3050.3050.0440.1951.0000.147
Victima0.1660.0000.0000.2170.0830.0000.1110.0000.0000.0000.9730.1471.000

Missing values

2024-10-17T13:03:21.399358image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-17T13:03:21.950378image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-17T13:03:22.334487image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Cantidad de victimasFechaAñoMesDíaHoraHHLugar del hechoTipo de calleCalleAlturaCruceDirección normalizadaComunaLongitudLatitudParticipantesVictimaAcusadoCoordenadas
Id
2016-000112016-01-012016111900-01-01 04:00:004.0AV PIEDRA BUENA Y AV FERNANDEZ DE LA CRUZAVENIDAPIEDRA BUENA AV.NaNFERNANDEZ DE LA CRUZ, F., GRAL. AV.PIEDRA BUENA AV. y FERNANDEZ DE LA CRUZ, F., GRAL. AV.8.0-58.47533969-34.68757022MOTO-AUTOMOTOAUTO-34.68757022, -58.47533969
2016-000212016-01-022016121900-01-01 01:15:001.0AV GRAL PAZ Y AV DE LOS CORRALESGRAL PAZPAZ, GRAL. AV.NaNDE LOS CORRALES AV.PAZ, GRAL. AV. y DE LOS CORRALES AV.9.0-58.50877521-34.66977709AUTO-PASAJEROSAUTOPASAJEROS-34.66977709, -58.50877521
2016-000312016-01-032016131900-01-01 07:00:007.0AV ENTRE RIOS 2034AVENIDAENTRE RIOS AV.2034.0NaNENTRE RIOS AV. 20341.0-58.39040293-34.63189362MOTO-AUTOMOTOAUTO-34.63189362, -58.39040293
2016-000412016-01-1020161101900-01-01 00:00:000.0AV LARRAZABAL Y GRAL VILLEGAS CONRADOAVENIDALARRAZABAL AV.NaNVILLEGAS, CONRADO, GRAL.LARRAZABAL AV. y VILLEGAS, CONRADO, GRAL.8.0-58.46503904-34.68092974MOTO-SDMOTONaN-34.68092974, -58.46503904
2016-000512016-01-2120161211900-01-01 05:20:005.0AV SAN JUAN Y PRESIDENTE LUIS SAENZ PEÑAAVENIDASAN JUAN AV.NaNSAENZ PE?A, LUIS, PRES.SAN JUAN AV. y SAENZ PEÑA, LUIS, PRES.1.0-58.38718297-34.62246630MOTO-PASAJEROSMOTOPASAJEROS-34.62246630, -58.38718297
2016-000812016-01-2420161241900-01-01 18:30:0018.0AV 27 DE FEBRERO Y AV ESCALADAAVENIDA27 DE FEBRERO AV.NaNESCALADA AV.27 DE FEBRERO AV. y ESCALADA AV.8.0-58.44451316-34.68475866MOTO-OBJETO FIJOMOTOOBJETO FIJO-34.68475866, -58.44451316
2016-000912016-01-2420161241900-01-01 19:10:0019.0NOGOYA Y JOAQUIN V. GONZALESCALLENOGOYANaNGONZALEZ, JOAQUIN V.NOGOYA y GONZALEZ, JOAQUIN V.11.0-58.50095869-34.60825440MOTO-AUTOMOTOAUTO-34.60825440, -58.50095869
2016-001012016-01-2920161291900-01-01 15:20:0015.0AV GENERAL PAZ Y AV DE LOS CORRALESGRAL PAZPAZ, GRAL. AV.NaNDE LOS CORRALES AV.PAZ, GRAL. AV. y DE LOS CORRALES AV.9.0-58.50877521-34.66977709MOTO-AUTOMOTOAUTO-34.66977709, -58.50877521
2016-001212016-02-082016281900-01-01 01:20:001.0AV BELGRANO Y BERNARDO DE IRIGOYENAVENIDABELGRANO AV.NaNIRIGOYEN, BERNARDO DEBELGRANO AV. e IRIGOYEN, BERNARDO DE1.0-58.38048577-34.61303893MOTO-CARGASMOTOCARGAS-34.61303893, -58.38048577
2016-001312016-02-1020162101900-01-01 11:30:0011.0AV ENTRE RIOS 1366AVENIDAENTRE RIOS AV.1366.0NaNENTRE RIOS AV. 13661.0-58.39114932-34.62477387PEATON-AUTOPEATONAUTO-34.62477387, -58.39114932
Cantidad de victimasFechaAñoMesDíaHoraHHLugar del hechoTipo de calleCalleAlturaCruceDirección normalizadaComunaLongitudLatitudParticipantesVictimaAcusadoCoordenadas
Id
2021-008812021-12-0120211211900-01-01 15:40:0015.0AV. MOROE Y 3 DE FEBREROCALLEMONROENaN3 DE FEBREROMONROE y 3 DE FEBRERO13.0-58.45531707-34.55555257MOTO-AUTOMOTOAUTO-34.55555257, -58.45531707
2021-008912021-12-0220211221900-01-01 01:10:001.0AV. GAONA 3655AVENIDAGAONA AV.3655.0NaNGAONA AV. 365511.0-58.47633683-34.62140594MOTO-AUTOMOTOAUTO-34.62140594, -58.47633683
2021-009012021-12-10202112101900-01-01 11:45:0011.0AV. 9 DE JULIO Y LAVALLEAVENIDA9 DE JULIO AV.NaNLAVALLE9 DE JULIO AV. y LAVALLE1.0-58.38188582-34.60256036PEATON-PASAJEROSPEATONPASAJEROS-34.60256036, -58.38188582
2021-009112021-12-11202112111900-01-01 23:00:0023.0BAIGORRIA Y VICTOR HUGOCALLEBAIGORRIANaNHUGO, VICTORBAIGORRIA y HUGO, VICTOR10.0-58.51989389-34.62284918MOTO-AUTOMOTOAUTO-34.62284918, -58.51989389
2021-009212021-12-12202112121900-01-01 06:20:006.0AV. RIVADAVIA Y AV. PUEYRREDONAVENIDARIVADAVIA AV.NaNPUEYRREDON AV.RIVADAVIA AV. y PUEYRREDON AV.3.0-58.40596860-34.61011987PEATON-AUTOPEATONAUTO-34.61011987, -58.40596860
2021-009312021-12-13202112131900-01-01 17:10:0017.0AV. RIESTRA Y MOMAVENIDARIESTRA AV.NaNMOMRIESTRA AV. y MOM7.0-58.43353773-34.64561636MOTO-AUTOMOTOAUTO-34.64561636, -58.43353773
2021-009412021-12-20202112201900-01-01 01:10:001.0AU DELLEPIANE Y LACARRAAUTOPISTADELLEPIANE, LUIS, TTE. GRAL.NaNLACARRA AV.DELLEPIANE, LUIS, TTE. GRAL. y LACARRA AV.9.0-58.46739825-34.65117757MOTO-AUTOMOTOAUTO-34.65117757, -58.46739825
2021-009512021-12-30202112301900-01-01 00:43:000.0AV. GAONA Y TERRADAAVENIDAGAONA AV.NaNTERRADAGAONA AV. y TERRADA11.0-58.47293407-34.61984745MOTO-CARGASMOTOCARGAS-34.61984745, -58.47293407
2021-009612021-12-15202112151900-01-01 10:30:0010.0AV. EVA PERON 4071AVENIDAPERON, EVA AV.4071.0NaNPERON, EVA AV. 40719.0-58.47066794-34.65021673AUTO-CARGASAUTOCARGAS-34.65021673, -58.47066794
2021-009712021-11-18202111181900-01-01 06:10:006.0PADRE CARLOS MUJICA 709CALLEPADRE CARLOS MUJICA709.0NaNPADRE CARLOS MUGICA 7091.0-58.37976155-34.58679619BICICLETA-AUTOBICICLETAAUTO-34.58679619, -58.37976155